1 Summary

1.1 Motivation

Ideal levels of green space may differ depending on density. At the global scale, there is a need to describe greenness appropriate for its region and population-density context. Such knowledge would identify feasible target areas for improved greening at the local level allowing to estimate the health benefits of such scenarios.

1.2 Objective

Estimate the health impacts of urban green scenarios based on population density-stratified measures of greenness in cities around the world.

1.3 Methods

1.3.1 Data sources

1.3.2 Approach

  • Stratify by biome, then by city, then by Landscan population category and then:

  • Measure tertiles of NDVI in each biome-city-pop-group stratum.

  • Scenario:

    • Set the NDVI of pixels in the bottom two tertiles to the NDVI value of the 83rd percentile (median of top tertile). In other words, only intervene upon pixels in the bottom two tertiles for that population category for that city for that biome. Note there are a few cities where biome varies within city.

    • The idea is that this would be a realistic intervention given the target NDVI is relative to the biome, city, and population density category.

  • Conduct HIA using mean of Landscan populatoin values in that category (also plan to use min/max for uncertainty analyses).

1.4 Status and next steps

HIA analysis complete for continental USA (48 states+DC) following those steps.

Working on expanding to global.

Discussion question: consider restricting to cities above a certain population?

2 Figures and tables

2.1 Depiction of data sources

2.1.1 Map of biomes (Continental USA)

2.1.2 Map of Landscan population categories (Colorado)

The map visualizes values categories coded 1-8 for easier visualization. The corresponding population categories appear in the table below.

## # A tibble: 9 × 4
##   pop_cat_1_8 pop_cat_min_val pop_cat_max_val pop_cat_mean_val
##         <dbl>           <dbl>           <dbl>            <dbl>
## 1           0               0               0              0  
## 2           1               1               5              3  
## 3           2               6              25             15.5
## 4           3              26              50             38  
## 5           4              51             100             75.5
## 6           5             101             500            300. 
## 7           6             501            2500           1500. 
## 8           7            2501            5000           3750. 
## 9           8            5001          185000          95000.

2.2 Map of global urban boundaries (Colorado)

Data are large, so only mapping Colorado. Visualize the area (square kilometers) of urban boundaries.

2.3 Results (Continental USA)

2.3.1 Results by biome

Table 2.1: HIA results by biome - Continental USA
biome name imp pop cat mean val scaled pop cat min val scaled pop cat max val scaled ndvi 2019 mean ndvi 2019 sd ndvi diff mean deaths baseline mean deaths baseline min deaths baseline max deaths prevented mean deaths prevented min deaths prevented max deaths prevented per 1k pop mean deaths prevented per 1k pop min deaths prevented per 1k pop max
Deserts & Xeric Shrublands 18,663,810 5,291,608.8 32,036,011 0.36 0 0 204,519.661 57,986 351,053 4,876.72 1,446.31 8,307.12 0 0 0.259
Flooded Grasslands & Savannas 8,635,230 2,051,279.3 15,219,180 0.58 0 0 94,625.599 22,478 166,773 3,827.79 731.49 6,924.09 0 0 0.455
Mangroves 85,168 29,658.1 140,677 0.67 0 0 933.274 325 1,542 16.64 5.58 27.71 0 0 0.197
Mediterranean Forests, Woodlands & Scrub 55,446,619 11,291,656.5 99,601,582 0.47 0 0 607,588.893 123,735 1,091,443 25,041.28 4,638.83 45,443.73 0 0 0.456
Temperate Broadleaf & Mixed Forests 150,389,494 32,467,659.1 268,311,329 0.72 0 0 1,647,981.202 355,783 2,940,179 57,880.88 10,179.02 105,582.73 0 0 0.394
Temperate Conifer Forests 15,525,499 4,349,817.3 26,701,182 0.68 0 0 170,129.778 47,666 292,594 4,998.94 1,313.66 8,684.23 0 0 0.325
Temperate Grasslands, Savannas & Shrublands 120,739,102 32,153,112.2 209,325,092 0.66 0 0 1,323,069.620 352,337 2,293,803 40,784.45 9,814.30 71,754.59 0 0 0.343
Tropical & Subtropical Grasslands, Savannas & Shrublands 10,619,909 2,901,686.3 18,338,133 0.60 0 0 116,373.895 31,797 200,951 3,752.49 957.26 6,547.71 0 0 0.357

2.3.2 Results by city

Cities above 1,000,000 people (per Landscan) sorted ascending by deaths prevented per 1k pop (top 10)

## # A tibble: 100 × 17
##    ORIG_FID city_name    pop_c…¹ pop_c…² pop_c…³ death…⁴ death…⁵ death…⁶ death…⁷
##       <dbl> <chr>          <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1    54733 <NA>          1.68e3  6.04e2  2.76e3  1.85e1  6.62e0  3.03e1 1.36e+0
##  2    60310 New York Ci…  1.26e7  9.17e5  2.43e7  1.38e5  1.01e4  2.66e5 9.50e+3
##  3    60228 Dayton        4.49e2  1.51e2  7.47e2  4.92e0  1.65e0  8.19e0 3.10e-1
##  4    53567 Yuma          7.35e4  2.64e4  1.21e5  8.06e2  2.90e2  1.32e3 4.94e+1
##  5    53578 El Centro     3.73e4  1.71e4  5.75e4  4.09e2  1.88e2  6.30e2 2.33e+1
##  6    61415 Reading       4.09e5  8.99e4  7.28e5  4.48e3  9.85e2  7.98e3 2.50e+2
##  7    53575 Brawley       1.98e4  7.56e3  3.21e4  2.17e2  8.28e1  3.52e2 1.21e+1
##  8    53570 Blythe        9.42e3  3.15e3  1.57e4  1.03e2  3.45e1  1.72e2 5.74e+0
##  9    53947 San Jose      1.23e7  2.37e6  2.23e7  1.35e5  2.59e4  2.44e5 6.87e+3
## 10    55562 Council Blu…  7.01e4  2.38e4  1.16e5  7.68e2  2.60e2  1.28e3 3.85e+1
## # … with 90 more rows, 8 more variables: deaths_prevented_min <dbl>,
## #   deaths_prevented_max <dbl>, ndvi_2019_mean <dbl>, ndvi_2019_sd <dbl>,
## #   ndvi_diff_mean <dbl>, deaths_prevented_per_1k_pop_mean <dbl>,
## #   deaths_prevented_per_1k_pop_min <dbl>,
## #   deaths_prevented_per_1k_pop_max <dbl>, and abbreviated variable names
## #   ¹​pop_cat_mean_val_scaled, ²​pop_cat_min_val_scaled, ³​pop_cat_max_val_scaled,
## #   ⁴​deaths_baseline_mean, ⁵​deaths_baseline_min, ⁶​deaths_baseline_max, …

2.3.3 Map: Deaths prevented per 1,000 population by city

Results are presented at the level of the global urban boundary. Following the methods described above, they are first stratified by biome and are thus relative to biome within urban boundary if biome varies within urban boundary.

2.4 Results (Global)

2.4.1 How many cities included?

## [1] 16071

2.4.2 Popluation distribution of cities included

Based on summarized Landscan 2019 data (mean of categories)

##  pop_cat_mean_val_scaled
##  Min.   :        0      
##  1st Qu.:     9326      
##  Median :    25253      
##  Mean   :   583206      
##  3rd Qu.:   247831      
##  Max.   :216444002

2.4.3 Area distribution of cities included

##     area_km2        
##  Min.   :    1.000  
##  1st Qu.:    1.426  
##  Median :    2.308  
##  Mean   :   12.364  
##  3rd Qu.:    5.248  
##  Max.   :10603.920

2.4.4 Results by biome

Table 2.2: HIA results by biome - global
biome name imp pop cat mean val scaled pop cat min val scaled pop cat max val scaled ndvi 2019 mean ndvi 2019 sd ndvi diff mean deaths baseline mean deaths baseline min deaths baseline max deaths prevented mean deaths prevented min deaths prevented max deaths prevented per 1k pop mean deaths prevented per 1k pop min deaths prevented per 1k pop max
Boreal Forests/Taiga 42,445,838 5,842,508.2 79,049,168 0.63 0 0 637,921.55 85,642 1,190,201 21,221.24 2,597.70 39,844.78 0 0 0.504
Deserts & Xeric Shrublands 974,588,688 71,091,101.5 1,878,086,275 0.35 0 0 8,147,220.76 605,832 15,688,610 247,900.63 17,765.39 478,035.88 0 0 0.255
Flooded Grasslands & Savannas 139,028,664 11,355,698.0 266,701,630 0.51 0 0 1,262,691.62 106,815 2,418,568 61,598.81 4,583.87 118,613.75 0 0 0.445
Mangroves 133,574,785 8,296,182.6 258,853,388 0.50 0 0 1,300,874.79 80,883 2,520,866 45,249.84 2,749.43 87,750.25 0 0 0.339
Mediterranean Forests, Woodlands & Scrub 633,864,528 61,760,057.9 1,205,968,998 0.48 0 0 6,043,413.66 615,416 11,471,411 201,717.07 20,018.78 383,415.36 0 0 0.318
Montane Grasslands & Shrublands 107,496,806 9,036,996.3 205,956,615 0.48 0 0 1,123,455.55 96,937 2,149,974 32,998.87 2,778.90 63,218.83 0 0 0.307
Temperate Broadleaf & Mixed Forests 3,389,195,742 338,797,610.6 6,439,593,874 0.63 0 0 36,802,897.59 3,773,645 69,832,150 1,398,139.44 133,912.77 2,662,366.12 0 0 0.413
Temperate Conifer Forests 74,655,009 10,719,288.7 138,590,730 0.64 0 0 743,984.99 114,098 1,373,872 20,851.44 3,183.44 38,519.43 0 0 0.278
Temperate Grasslands, Savannas & Shrublands 511,850,516 68,907,867.2 954,793,164 0.61 0 0 5,721,528.99 787,793 10,655,265 178,175.10 22,957.66 333,392.54 0 0 0.349
Tropical & Subtropical Coniferous Forests 51,658,641 3,563,687.0 99,753,596 0.53 0 0 498,612.54 34,412 962,813 20,831.70 1,382.72 40,280.68 0 0 0.404
Tropical & Subtropical Dry Broadleaf Forests 562,417,155 37,541,926.5 1,087,292,383 0.53 0 0 5,226,778.14 349,510 10,104,046 182,651.38 11,713.48 353,589.29 0 0 0.325
Tropical & Subtropical Grasslands, Savannas & Shrublands 386,293,796 32,490,621.1 740,096,971 0.52 0 0 3,986,628.92 332,601 7,640,657 120,812.56 9,876.29 231,748.83 0 0 0.313
Tropical & Subtropical Moist Broadleaf Forests 2,362,437,676 163,224,277.6 4,561,651,075 0.56 0 0 22,019,803.64 1,518,758 42,520,849 935,111.93 61,880.19 1,808,343.67 0 0 0.396
Tundra 717,884 110,184.8 1,325,584 0.72 0 0 10,793.14 1,565 20,021 217.37 36.66 398.08 0 0 0.300
NA 2,471,249 318,812.6 4,623,685 0.58 0 0 31,008.45 3,950 58,067 728.80 98.01 1,359.58 0 0 0.294

2.4.5 Results by city

Cities above 1,000,000 people (per Landscan) sorted ascending by deaths prevented per 1k pop (top 20)

Table 2.3: HIA results by city - top 20 deaths prevented per pop.
city name country name pop cat mean val scaled pop cat min val scaled pop cat max val scaled ndvi 2019 mean ndvi 2019 sd ndvi diff mean deaths baseline mean deaths baseline min deaths baseline max deaths prevented mean deaths prevented min deaths prevented max deaths prevented per 1k pop mean deaths prevented per 1k pop min deaths prevented per 1k pop max
Onitsha Nigeria 3,187,841 204,388.64 6,171,293 0.4939915 0.1671285 0.2559899 45,532.43 2,919.3153 88,145.55 3,912.3732 231.41436 7,593.332 1.2272800 1.1322271 1.2304281
Hiroshima Japan 6,614,990 512,117.83 12,717,862 0.5387542 0.1686172 0.2446807 95,624.06 7,403.0025 183,845.11 7,156.3521 529.55597 13,783.148 1.0818387 1.0340510 1.0837630
Shizuoka Japan 2,053,436 282,266.54 3,824,606 0.5065099 0.1464315 0.2373826 29,683.78 4,080.3498 55,287.22 2,146.3764 287.54129 4,005.212 1.0452607 1.0186871 1.0472219
Yukuhashi Japan 4,404,142 510,888.95 8,297,394 0.5554971 0.1347714 0.1949242 63,664.79 7,385.2382 119,944.33 4,477.2088 433.55027 8,520.867 1.0165905 0.8486194 1.0269329
Iasi Romania 1,939,607 132,674.85 3,746,539 0.6569125 0.0852292 0.1181005 30,976.09 2,118.8563 59,833.33 1,787.9369 108.35761 3,467.516 0.9218037 0.8167155 0.9255252
Niigata Japan 1,635,879 270,288.51 3,001,469 0.5359789 0.1664703 0.2078614 23,647.71 3,907.1994 43,388.23 1,495.5233 252.66048 2,738.386 0.9142017 0.9347807 0.9123485
Osaka Japan 94,276,095 6,022,441.90 182,529,748 0.4221044 0.1599716 0.2216958 1,362,823.40 87,058.3869 2,638,588.42 84,822.5259 5,488.06867 164,156.983 0.8997246 0.9112697 0.8993437
Saint Petersburg Russian Federation 13,756,808 1,086,967.32 26,426,649 0.5622628 0.1297052 0.1569580 215,416.14 17,020.6852 413,811.59 11,734.5300 872.86029 22,596.200 0.8529980 0.8030235 0.8550535
Kagoshima Japan 3,068,363 241,436.92 5,895,289 0.5516118 0.1506268 0.1893906 44,355.22 3,490.1306 85,220.31 2,539.9483 189.28120 4,890.615 0.8277861 0.7839779 0.8295802
Genoa Italy 2,578,400 173,033.95 4,983,767 0.6698486 0.1264657 0.1350290 32,254.26 2,164.5524 62,343.98 2,021.5811 115.66873 3,927.493 0.7840447 0.6684742 0.7880572
Okayama Japan 1,933,859 246,896.47 3,620,821 0.5089071 0.1466243 0.2051750 27,955.21 3,569.0521 52,341.36 1,485.5312 224.89293 2,746.170 0.7681696 0.9108795 0.7584384
Acapulco de Juarez Mexico 2,369,231 151,491.28 4,586,970 0.6419416 0.1228943 0.1558808 23,785.05 1,520.8431 46,049.26 1,806.1239 102.86986 3,509.378 0.7623251 0.6790481 0.7650754
Al Mansurah Egypt 2,329,948 136,709.47 4,523,188 0.4995927 0.1762782 0.2607673 20,300.42 1,191.1248 39,409.71 1,766.0492 97.87057 3,434.228 0.7579778 0.7159019 0.7592495
Sapporo Japan 11,714,803 1,090,762.62 22,338,843 0.5461218 0.1607430 0.2131454 169,345.23 15,767.6962 322,922.77 8,782.7019 918.88813 16,646.516 0.7497098 0.8424272 0.7451826
Tanta Egypt 2,152,840 120,278.17 4,185,402 0.4960566 0.1763817 0.2546069 18,757.31 1,047.9619 36,466.65 1,606.7491 85.65710 3,127.841 0.7463392 0.7121583 0.7473214
Helsinki Finland 2,335,593 389,504.39 4,281,681 0.6570849 0.0815809 0.1105266 28,854.02 4,811.9551 52,896.08 1,731.0134 195.01530 3,267.012 0.7411453 0.5006755 0.7630208
E’zhou China 1,315,944 96,474.24 2,535,415 0.5582173 0.1201188 0.1698263 11,981.24 878.3665 23,084.12 974.8524 62.00324 1,887.702 0.7408006 0.6426922 0.7445337
Al Mahallah al Kubra Egypt 2,319,523 130,334.58 4,508,712 0.4957914 0.1633275 0.2624511 20,209.59 1,135.5815 39,283.59 1,705.4343 92.33619 3,318.532 0.7352520 0.7084551 0.7360267
Ikeja Nigeria 34,497,185 1,924,017.97 67,070,352 0.4606142 0.1240450 0.1681278 492,728.74 27,481.0529 957,976.44 24,764.7971 1,353.10312 48,176.491 0.7178788 0.7032695 0.7182979
Belgrade Serbia 7,275,108 516,114.31 14,034,101 0.5683576 0.1030806 0.1325384 126,214.41 8,953.9656 243,474.86 5,221.9016 366.59074 10,077.212 0.7177765 0.7102898 0.7180519

2.4.6 Map: Deaths prevented per 1,000 population by city (Top 100) among cities with 1 million+ population